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COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots
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DOI：10.1109/TIE.2021.3070497.png)
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摘要
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Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when the initial error is large and the overlapping area is low. In this article, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. First, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using expectation-maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on semantic KITTI benchmarks and real-world experiments show the improved accuracy, convergence, and robustness.
Keyword:
Semantics
Robots
Robot kinematics
Collaboration
Robot sensing systems
Three-dimensional displays
Geometry
Collaborative robots
relative localization
semantic mapping
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期刊
IF:
7.2
论文数:
1.8W
被引数:
9.8W
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